If you missed yesterday’s introduction to PER, check it out. It seems to have generated a mostly positive response, but some legitimate questions were raised, which I’ll address now.

Here’s one from Dan Hennessey on Twitter: Aren’t you concerned PER doesn’t account for D? It’s been well documented that ER and errors don’t evaluate D well.

Yes, this is a flaw. But it’s one we’ll have to live with until a better way to track earned runs is created. When developing PER, I specifically wanted to create something which could be used to grade an individual game. And unfortunately, tracking earned runs, hits, errors, etc. is our only option.

Stats such as FIP do a much better job of predicting the future than they do evaluating the past. And I mostly wanted PER to be an evaluation of what happened, rather than what will happen. Additionally, when getting into stats such as xFIP, there is guesswork involved that I’m not entirely on board with as a legitimate tool. It’s definitely a starting point when trying to predict the future, but it tends to group everyone towards the middle.

Take Tim Lincecum as an example:

Year

xFIP

PER

PER Win%

2007

3.81

.753

.567

2008

3.13

.795

.652

2009

2.83

.813

.695

2010

3.09

.770

.603

2011

3.36

.787

.634

2012

3.82

.716

.488

2013

3.56

.752

.554

2014

3.68

.708

.460

FanGraphs considers any xFIP above 3.75 to be “above average” and an indication that the pitcher should bounce back from a bad year if their actual ERA is above that level. Well, why hasn’t that happened for Lincecum? He continues to struggle and has yet to regain his dominant form from years past. To further complicate things, the Giants defense has rated fairly well during his worst years. In 2012, they ranked 8th in UZR.

The issue here is that stats like xFIP make assumptions. In this case, the assumption is that Lincecum’s home run/fly ball rate is fluke, and it’s adjusted down to league average based on the xFIP formula. But after three straight years of giving up long balls, it’s no longer a fluke. It’s a skill that he’s lost the ability to control.

Ultimately, I think every statistic needs to be looked at for its strengths and weaknesses. PER is an upgrade over Game Score and Quality Starts in terms of an ability to judge an individual performance, and it’s a strong tool for evaluating full seasons and careers as well. But PER does not attempt to replace more predictive stats such as xFIP. These types of stats should be used together to tell the full story and give a more complete picture of a pitcher’s performance.

From Ryan Tirk: Can you use this stat to evaluate a team’s starting 5? In other words, how does a group number, encompassing all starting pitchers on a team correlate to that team’s winning percentage?

Yes, it works for the staff the same way it works for individuals. The Indians currently rank 26th in the majors with a team PER of .727.

From Jim M: “per” sounds too basketbally, since they already have a stat of the same name in that sport. how about “rep” or Rating the Efficiency of Pitching. sounds more sporty too. getting your reps in. quality reps, etc

I thought about different names, but I wanted the name to be as straight forward as possible. We’re tracking efficiency, so PER just made the most sense.

Now let’s use PER to look at the Indians pitching staff this year. Here are their stats…

SP

PER

PER Win%

PER Win Probability

J.Tomlin

.788

.633

+.265

C.Kluber

.783

.629

+.259

T.House

.742

.523

+.045

T.Bauer

.740

.522

+.043

J.Masterson

.722

.499

-.002

Z.McAllister

.659

.433

-.135

C.Carrasco

.697

.420

-.159

D.Salazar

.687

.415

-.171

Quick PER refresher:
PER = the raw stat, roughly on a scale of 0 to 1.000
PER Win% = team’s average expected win percentage based on corresponding PER
PER Win Probability = assuming 50/50 odds a start of game, impact SP had on team’s win probability

I’m guessing no one is surprised by these stats. Tomlin and Kluber have been head and shoulders above the rest of the staff so far this season.

All three columns of the table are basically the same stat, just in different format. But the third column really shows the gap between the top and bottom of the staff.

Here are a few notes on the Tribe starters…

In 13 starts this season, Masterson has had a negative impact on their win probability six times. In two of those starts, he gave them an expected win percentage below 20 percent.

In six starts, Tomlin has produced a positive win probability five times. Even in his worst start, he only decreased their win probability by 3.8 percent.

Due to the small sample size, Bauer’s stats are dramatically skewed by his start against the Orioles. He decreased their win probability by 42.4 percent, giving them an expected win percentage against the Orioles of just 28.8 percent.

Excluding the start against the O’s, Bauer has a win probability of +.160 in his other four starts.

Salazar was a complete train wreck before being sent to the minors. He decreased their win probability in six of eight starts, and five of those starts decreased their win probability by over 25 percent.